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Focal liver lesion diagnosis with deep learning and multistage CT imaging

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机构： [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China [2]School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. [3]Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China. [4]Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China. [5]Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China. [6]Department of Radiology, Leshan People’s Hospital, Leshan, Sichuan, China. [7]Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou, China. [8]Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, Zhejiang, China. [9]Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.

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Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.© 2024. The Author(s).

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大类 | 1 区

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小类 | 1 区综合性期刊

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第一作者机构： [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China

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通讯作者：

通讯机构： [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China [3]Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China. [8]Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, Zhejiang, China. [9]Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.

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